https://github.com/alisatodorova/ml-groupwork-2
Group work on MultiLayer Perceptron (MLP) and Hyperparameters Optimization
https://github.com/alisatodorova/ml-groupwork-2
binary-cross-entropy-loss hyperparameter-optimization machine-learning mlp multilayer-perceptron relu sigmoid-function
Last synced: 7 months ago
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Group work on MultiLayer Perceptron (MLP) and Hyperparameters Optimization
- Host: GitHub
- URL: https://github.com/alisatodorova/ml-groupwork-2
- Owner: alisatodorova
- Created: 2024-07-31T12:30:45.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-31T13:44:27.000Z (about 1 year ago)
- Last Synced: 2025-01-22T04:43:22.627Z (9 months ago)
- Topics: binary-cross-entropy-loss, hyperparameter-optimization, machine-learning, mlp, multilayer-perceptron, relu, sigmoid-function
- Language: Jupyter Notebook
- Homepage:
- Size: 4.17 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# ML-GroupWork-2
Group work on MultiLayer Perceptron (MLP) and Hyperparameters Optimization.
See **IS-ML-Take-Home-Assignment-2.pdf** for tasks and instructions. See **Group_13-ML_Assignment_2.pdf** for our report.
Part A includes:
- Implementation of a MultiLayer Perceptron (MLP) model from scratch, which can perform a two-class classification task on the given dataset.
- Activation Functions: Rectified Linear Unit (ReLU) and Sigmoid activation function
- Binary Cross-Entropy Loss function
- Mean Square error loss
- Huber loss
Part B includes:
- Confusion Matrix
- Training and Validation Losses
- Preprocessing Techniques: Normalization and One-Hot Encoding
- Hyperparameter Tuning
- Heatmap Visualization